Adaptive multiscale predictive modelling

doi:10.1017/S096249291800003X

Adaptive multiscale predictive modelling

2018-05-04

44

PRE

AI

J

J. Tinsley Oden *

DOI：10.1017/S096249291800003X

Original

Original request for help

Save

Abstract

En 中文

The use of computational models and simulations to predict events that take place in our physical universe, or to predict the behaviour of engineered systems, has significantly advanced the pace of scientific discovery and the creation of new technologies for the bene fit of humankind over recent decades, at least up to a point. That 'point' in recent history occurred around the time that the scientific community began to realize that true predictive science must deal with many formidable obstacles, including the determination of the reliability of the models in the presence of many uncertainties. To develop meaningful predictions one needs relevant data, itself possessing uncertainty due to experimental noise; in addition, one must determine model parameters, and concomitantly, there is the overriding need to select and validate models given the data and the goals of the simulation. This article provides a broad overview of predictive computational science within the framework of what is often called the science of uncertainty quantification. The exposition is divided into three major parts. In Part 1, philosophical and statistical foundations of predictive science are developed within a Bayesian framework. There the case is made that the Bayesian framework provides, perhaps, a unique setting for handling all of the uncertainties encountered in scientific prediction. In Part 2, general frameworks and procedures for the calculation and validation of mathematical models of physical realities are given, all in a Bayesian setting. But beyond Bayes, an introduction to information theory, the maximum entropy principle, model sensitivity analysis and sampling methods such as MCMC are presented. In Part 3, the central problem of predictive computational science is addressed: the selection, adaptive control and validation of mathematical and computational models of complex systems. The Occam Plausibility Algorithm, OPAL, is introduced as a framework for model selection, calibration and validation. Applications to complex models of tumour growth are discussed.

Keywords:

ORIENTED ERROR ESTIMATION

SENSITIVITY-ANALYSIS

VALIDATION

SELECTION

CALIBRATION

FOUNDATIONS

HALLMARKS

FRAMEWORK

SYSTEMS

THEOREM

Adaptive multiscale predictive modelling

Abstract

AI Summary

Journal

Organization

Cited Papers